{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JALCAYJACD26HUKEMPJHURX5QZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d"},"schema_version":"1.0","source":{"id":"2406.04824","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"arxiv_version","alias_value":"2406.04824v2","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_12","alias_value":"JALCAYJACD26","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_16","alias_value":"JALCAYJACD26HUKE","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_8","alias_value":"JALCAYJA","created_at":"2026-07-05T08:38:38Z"}],"graph_snapshots":[{"event_id":"sha256:17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3","target":"graph","created_at":"2026-07-05T08:38:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.04824/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs ","authors_text":"Alan Malek, Alexis Bellot, Eleni Sgouritsa, Francisco J. R. Ruiz, Ira Ktena, Jessica Schrouff, Silvia Chiappa, Virginia Aglietti","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title":"FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04824","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3","target":"record","created_at":"2026-07-05T08:38:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d"},"schema_version":"1.0","source":{"id":"2406.04824","kind":"arxiv","version":2}},"canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","first_computed_at":"2026-07-05T08:38:38.503403Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:38.503403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NmjFb97FLe/6HXR2tZRr4z5DE7hpjzlOAv75jx8VB9UU+nVdGhZZPifEpmpY+BvhYfNgBzT6lFw6dICpxF52AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:38.503876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.04824","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3","sha256:17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3"],"state_sha256":"25e1178ff6d5fe95d98e9b4bbcaa739e7012cfcef8cf4868e568b1b9303ab55c"}